Global classification of sonic logs

ABSTRACT

A method of determining the sonic slowness of a formation traversed by a borehole comprising generating tracks from sonic waveform peaks received at a plurality of depths wherein the peaks that are not classified prior to tracking is set forth. A method for generating a slowness versus depth log is generated for waveform arrivals by classifying long tracks, classifying small tracks; classifying tracks that overlap; filling in gaps; and creating a final log is disclosed. In further improvements, non-classified tracks and interpolation are used to fill in gaps.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to co-owned U.S. Pat. Nos. 4,809,236,5,661,696; 5,594,706; 5,587,966; and 5,278,805, and U.S. patentapplication Ser. Nos. 09/591,405 and 09/678,454; and PCT/IB00/00353, thecomplete disclosures of which are hereby incorporated by reference.

BACKGROUND OF THE INVENTION

This invention relates to sonic well logging used in the hydrocarbonwell exploration. More particularly, the invention relates to methodsfor processing sonic well log waveforms.

Sonic logging of wells is well known in hydrocarbon exploration. Sonicwell logs are generated using sonic tools typically suspended in amud-filled borehole by a cable. The sonic logging tool typicallyincludes a sonic source (transmitter), and a plurality of receivers(receiver array) that are spaced apart by several inches or feet. It isnoted that a sonic logging tool may include a plurality of transmittersand that sonic logging tools may be operated using a single transmitter(monopole mode), dual transmitters (dipole mode) or a plurality oftransmitters (multipole mode). A sonic signal is transmitted from thesonic source and detected at the receivers with measurements made everyfew inches as the tool is drawn up the borehole. The sonic signal fromthe transmitter enters the formation adjacent to the borehole and partof the sonic signal propagates in the borehole.

Sonic waves can travel through formations around the borehole inessentially two forms: body waves and surface waves. There are two typesof body waves that travel in formation: compressional and shear.Compressional waves, or P-waves, are waves of compression and expansionand are created when a formation is sharply compressed. Withcompressional waves, small particle vibrations occur in the samedirection the wave is traveling. Shear waves, or S-waves are waves ofshearing action as would occur when a body is struck from the side. Inthis case, rock particle motion is perpendicular to the direction ofwave propagation.

Surface waves are found in a borehole environment as complicatedborehole-guided waves coming from reflections of the source wavesreverberating in the borehole. The most common form of surface wave isthe Stoneley wave. In situations where dipole (directional) sources andreceivers are used, an additional flexural wave propagates along theborehole and is caused by the flexing action of the borehole in responseto the dipole signal form the source. It is noted that sonic waves alsowill travel through the fluid in the borehole and along the tool itself.With no interaction with the formation, these waves do not provideuseful information and may interfere with the waveforms of interest.

Typically, compressional (P-wave), shear (S-wave) and Stoneley arrivalsare detected by the receivers. The speeds at which these waves travelthrough the rock are controlled by rock mechanical properties such asdensity and elastic dynamic constants, and other formation propertiessuch as amount and type of fluid present in the rock, the makeup of therock grains and the degree of intergrain cementation. Thus by measuringthe speed of sonic wave propagation in a borehole, it is possible tocharacterize the surrounding formations by parameters relating to theseproperties. The information recorded by the receivers is typically usedto determine formation parameters such as formation slowness (theinverse of sonic speed) from which pore pressure, porosity, and otherdeterminations can be made. The speed or velocity of a sonic wave isoften expressed in terms of 1/velocity and is called “slowness.” Sincethe tools used to make sonic measurements in boreholes are of fixedlength, the difference in time (ΔT) taken for a sonic wave to travelbetween two points on the tool is directly related to the speed/slownessof the wave in the formation. In certain tools such as the DSI™ (DipoleSonic Imager) tool (a trademark owned by Schlumberger), the sonicsignals may be used to image the formation.

Details relating to sonic logging and log processing techniques are setforth in U.S. Pat. No. 4,131,875 to Ingram; U.S. Pat. No. 4,594,691 toKimball and Marzetta; U.S. Pat. No. 5,278,805 to Kimball; U.S. Pat. No.5,831,934 to Gill et al.; A. R. Harrison et al., “Acquisition andAnalysis of Sonic Waveforms From a Borehole Monopole and Dipole Source .. . ” SPE 20557, pp. 267-282 (September 1990); and C. V. Kimball and T.L. Marzetta, “Semblance Processing of Borehole Acoustic Array Data”,Geophvsics, Vol. 49, pp. 274-281 (March 1984), all of which areincorporated by reference herein in their entireties.

The response of any given one of receivers to a sonic signal from atransmitter is typically a waveform as shown in FIG. 1 for aneight-receiver array. Sonic waveforms 1 through 8 as received atdifferent receivers within the array are shown. The responses of theseveral receivers are staggered in time due to the different spacing ofthe receivers from the transmitter. The first arrivals 10 shown arecompressional waves, followed by the arrival of shear waves 12 and thenthe arrival of Stoneley waves 14. It will be appreciated that where thesonic signal detected is non-dispersive (e.g. P-waves and S-waves), thesignal obtained at each receiver will take the same or similar form.However, where the sonic signal is dispersive (e.g. Stoneley andflexural waves), the signal obtained at the different receivers willappear different.

In most formations, the sonic speeds in the tool and the wellbore mudare less than the sonic speed in the formation. In this typicalsituation, the compressional (P-waves), shear (S-waves), and Stoneley ortube wave arrivals and waves are detected by the receivers and areprocessed. Sometimes, the sonic speed in the formation is slower thanthe drilling mud; i.e., the formation is a “slow” formation. In thissituation, there is no refraction path available for the shear waves,and typically shear (S-waves) arrivals are not measurable at thereceivers. However, the shear slowness of the formation is still adesirable formation parameter to obtain. Although without shear wavesignal detection, direct measurement of formation shear slowness is notpossible but it may be determined from other measurements.

One way to obtain the slowness of a formation from an array of sonicwaveforms is to use slowness-time-coherence (STC) processing. One typeof STC processing is presented in U.S. Pat. No. 4,594,691, incorporatedherein in its entirety. STC processing is a full waveform analysistechnique that aims to find all propagating waves in a compositewaveform. The result of the process is a collection of semblance peaksin a slowness-time plane for various depths. At each depth the peaks maybe associated with different waveform arrivals. The processing adopts asemblance algorithm to detect arrivals that are coherent across thearray of receivers and estimates their slowness. The basic algorithmadvances a fixed-length time window across the waveforms in smalloverlapping steps through a range of potential arrival times. For eachtime position, the window position is moved out linearly in time, acrossthe array of receiver waveforms, beginning with a moveout correspondingto the fastest wave expected and stepping to the slowest wave expected.For each moveout, a coherence function is computed to measure thesimilarity of the waves within the window. When the window time and themoveout correspond to the arrival time and slowness of a particularcomponent, the waveforms within the window are almost identical,yielding a high value of coherence. In this way, the set of waveformsfrom the array is examined over a range of possible arrival times andslownesses for wave components.

STC processing produces coherence (semblance) contour plots in theslowness/arrival time plane. The semblance function relates the presenceor absence of an arrival with a particular slowness and particulararrival time. If the assumed slowness and arrival time do not coincidewith that of the measured arrival, the semblance takes on a smallervalue. Consequently, arrivals in the received waveforms manifestthemselves as local peaks in a plot of semblance versus slowness andarrival time. These peaks are typically found in a peak-finding routinediscussed in the aforementioned article by Kimball and Marzetta.

As the output of STC processing is a coherence plot, the coherence ofeach arrival can be used as a quality indicator, higher values implyinggreater measurement repeatability. When processing dipole waveforms, oneof the coherence peaks will correspond to the flexural mode but with aslowness that is always greater (slower) than the true shear slowness. Aprecomputed correction is used to remove this bias.

In simple STC processing, all receiver stations are considered. Anothertype of slowness-time-coherence is processing multi-shotslowness-time-coherence (MSTC) processing wherein sub-arrays of receiverstations within the receiver array are considered. MSTC processing isdescribed in U.S. patent application Ser. No. 09/678,454, incorporatedherein by reference in its entirety.

In the aforementioned methods, the same back-propagation and stackingtechniques are used regardless of whether the wave being analyzed is aP-wave, S-wave, or a Stoneley wave; i.e., regardless of whether the waveis non-dispersive or dispersive. Additional techniques are known toaddress dispersive waves. For dispersive waves, STC processing ismodified to take into account the effect of frequency and dispersion.

Bias-corrected STC as described in U.S. Pat. No. 5,229,939, incorporatedherein in its entirety, involves processing the flexural waveform usingSTC methods but correcting the non-dispersive processing results by afactor relating to the measured slowness and hole diameter, that is,post-processing the STC results. In particular, correction values areobtained by processing model waveforms with the STC techniques andcomparing the measured slowness with the formation shear slowness of themodel.

A second technique to provide slowness logging which accounts fordispersion is known as Dispersive Slowness Time Coherence (DSTC)processing or Quick DSTC (QDSTC) and presented in U.S. Pat. No.5,278,805, the contents of which are incorporated herein by reference.DTSC processing broadly comprises back-propagating detected dispersivewaveforms in the Fourier domain while accounting for dispersion and thenstacking the processed waveforms. DSTC processing has the ability to beapplied to non-dispersive waves such as monopole compressional or shearwaves. Since the first step required for DSTC processing is thecalculation or selection or an appropriate dispersion curve, all that isrequired is a dispersion curve that represents a non-dispersive wave,i.e., a flat “curve”.

The first step in slowness-time coherence processing is computingsemblance, a two-dimensional function of slowness and time, generallyreferred to as the STC slowness-time plane. The semblance is thequotient of the beamformed energy output by the array at slowness p (the“coherent energy”) divided by the waveform energy in a time window oflength T (the “total energy”). The semblance function is given byEquation (1) where x_(i)(t) is the waveform recorded by the i-threceiver of an M-receiver equally spaced array with inter-receiverspacing ΔZ. The array of waveforms {x_(i)(t)} acquired at depth zconstitutes a single frame of data. $\begin{matrix}{{\rho\left( {\tau,p} \right)} = \frac{\int_{\tau}^{\tau + T}{\left\lbrack {\sum\limits_{i = 0}^{M - 1}\quad{x_{i}\left( {t + {i\quad\Delta\quad{zp}}} \right)}} \right\rbrack^{2}\quad{\mathbb{d}t}}}{M{\int_{\tau}^{\tau + T}{\sum\limits_{k = 0}^{M - 1}{\left\lbrack {x_{i}\left( {t + {i\quad\Delta\quad{zp}}} \right)} \right\rbrack^{2}\quad{\mathbb{d}t}}}}}} & (1)\end{matrix}$The semblance ρ(τ,p) for a particular depth z is a function of time τand slowness p.

A second step is identifying peaks corresponding to high coherence onthe slowness-time plane. Peaks are identified by sweeping the plane witha peak mask. The peak mask is a parallelogram having a slope thatcorresponds to the transmitter-receiver spacing. A peak is defined as amaximum within the mask region. For each peak, five variables arerecorded: the slowness coordinate p, the time coordinate τ, thesemblance ρ(τ,p), the coherent energy (the numerator of Equation 1), andthe total energy (the denominator of Equation 1).

Peaks in coherence values signify coherent arrivals in the waveforms.For each depth, a contour plot of coherence as a function of slownessand time, referred to the slowness-time plane, can be made.Classification occurs when the slowness and arrival time at eachcoherence peak are compared with the propagation characteristicsexpected of the arrivals being sought and the ones that best agree withthese characteristics are retained. Classifying the arrivals in thismanner produces a continuous log of slowness versus depth.

Typically in prior art methods the slowness and arrival time at eachcoherence peak are compared with the propagation characteristics of theexpected arrivals and classified as to type of arrival and “labeled” or“tracked” as corresponding to compressional (P-wave), shear (S-wave) orStoneley waveform arrivals. Thus classified, the arrivals produce acontinuous log of slowness versus depth, referred to as a “track”, asequence of measurements composed of peaks identified as belonging tothe same arrival as shown in FIG. 2. Referring to FIG. 2, peak 20 isclassified as a compressional arrival and peak 22 is classified as ashear arrival and the classified peaks are joined to other arrivals ofthe same waveform in a slowness versus depth log. In prior art methods,the tracking composed two distinct steps 1) joining the peakscorresponding to the same waveform arrival in the track-search step tocompose a “track”, and 2) identifying the tracks by a name throughclassification of the tracks. In these methods, individual peaksrequired classification independent of the tracks.

Correct tracking of the peaks is a difficult process for a number ofreasons. Some of the peaks may correspond to spatial aliases rather thanthe arrival of real waveforms. Some of the peaks may actually be twopeaks close together. In general, a shortcoming with prior art methodsfor tracking is that small changes in sonic waveform data can causelarge differences in the final classification.

In a classification method referred to as local classification anddescribed in U.S. patent application Ser. No. 09/591,405 (hereinafter'405), the peaks are classified by referring to only two levels, thecurrent level and the previous level. This local classification of peaksof the tracks is independent of other non-adjacent peaks. Such aclassification, because of the limits of the Bayesian algorithm used,does not classify the whole track but just the adjacent peaks of thetrack at any particular time. These classified peaks are used togenerate a track and the track classified based on the classification ofthe peaks from which it is composed. The '405 method has the advantageof allowing classification to follow the usual data flow of theIntegrated Slowness Determination Process (ISDP) processing and isapplicable to well site implementation. Nevertheless in some situationslocal classification is not robust enough nor can defects like jumpsbetween two tracks corresponding to different arrivals or spikes on thefinal log be avoided. There are situations in which a different means ofclassification is desirable.

SUMMARY OF THE INVENTION

It is an object of the invention to provide methods for more accuratelytracking sonic waveform information. It is also an object of theinvention to provide methods for tracking sonic measurements intosequences that may be identified as belonging to a single arrival or“track”. It is a further object of the invention to provide methods ofwaveform analysis that can be performed automatically.

The present invention provides a method of determining the sonicslowness of a formation traversed by a borehole comprising generatingtracks from sonic waveform peaks received at a plurality of depths,wherein the peaks that are not classified prior to tracking. Generatingtracks may comprise classifying long tracks; classifying small tracks;classifying tracks that overlap; filling in gaps; and creating a finallog.

Embodiments of the present invention include using non-classified tracksto fill gaps and performing interpolation to fill gaps. An embodimentcomprises using time and slowness and not semblance for classification.In accordance with the present invention, tracks may be classifiedindependently of each other.

In a more specific embodiment, long tracks are classified using a methodcomprising fitting a distribution function on peaks of the track,calculating a mean and variance of the distribution, comparingdistribution of the data with a distribution of a model data andclassifying according to the model data if said comparison determinesthat the track data and model data are consistent.

In a further embodiment, small tracks are classified using a methodcomprising computing a 2-D median of the track, said median being apoint defined by corresponding coordinates in a slowness and timedomain; determining an intersection of the slowness and time domain witha model data distribution; defining the model in the slowness and timedomain as an ellipse; and classifying the small track based on aposition of the peak in relation to the model data.

Another embodiment comprise determining if there is a gap in a logcorresponding to a selected track at a depth range covered by a selectednon-classified track and filling the gap after determining if theselected non-classified track can be used to fill the gap. A furtherembodiment comprises determining if the selected gap can be used to fillthe gap by evaluating if the selected track is between upper part andlower part of a skeleton, wherein said skeleton comprises tracks thathave been classified so far. In a specific embodiment, long trackscomprise more than 20 arrival frames, small tracks comprise less than orequal to 20 frames and slowness and time are treated as 2D Gaussianrandom processes wherein the probability distribution of slowness andtime is measured by a 2D Kaman filter process at one depth based onmeasurements at a previous depth.

Additional objects and advantages of the invention will be apparent tothose skilled in the art upon reference to the detailed description andthe provided figures.

BRIEF DESCRIPTION OF THE FIGURES

The above objectives and advantages of the present invention will becomemore apparent by describing in attached figures in which:

FIG. 1 shows prior art waveforms from a receiver array.

FIG. 2 shows the prior art concept of tracking coherence peaks onslowness versus depth log and classification of the arrivals.

FIG. 3 shows examples of actual data tracks. A track may comprisecompressional, shear, Stoneley, flexural or dispersive arrivals. It isimportant to note that there is only one peak per level per expectedwaveform arrival.

FIG. 4 shows steps in the disclosed global classification method.

FIG. 5 shows an example of how a Gaussian function is fit on actualtrack peak waveform data.

FIGS. 6 a, 6 b and 6 c show the comparison of actual track data withmodel data.

FIG. 7 shows the classification of small tracks using 2-dimensionalmodels.

FIG. 8 shows an example of unused tracks for filling gaps.

DETAILED DESCRIPTION

Referring to FIG. 1, a typical waveform response of an eight-receiverarray to a sonic signal from a transmitter is shown. Although referenceis made to an eight-receiver array, it will be appreciated that thepresent method may be used with any number of receivers or any type ofsource. Using any type of slowness-time-coherence (STC) methodology,examples of which have been described herein, the waveform responses areprocessed and coherence peaks in the slowness-time plane determined.

The present method is used to generate a raw slowness or time trackcomprising all peaks at a particular depth, wherein the peaks are notpreviously classified. In this global classification, a raw track isconsidered as an individual object composed by peaks. These peaks aredefined using the semblance, the time and the slowness. In an embodimentof the disclosed technique, only time and slowness and not semblance areused for classification. These raw tracks may include peakscorresponding to compressional (P-waves), shear (S-waves), or Stoneleywaves at any particular depth. Referring to FIG. 3, a raw slowness track30 and raw time track 36 is shown. The present method includes all peakarrivals for each depth without previous classification of the peaks. Bythis approach, classification of the track is simpler than prior artmethods that require a comparison between and classification ofindividual peaks prior to joining peaks to a track. Once these rawslowness or time tracks have been generated, a method referred to asglobal classification and shown in FIG. 4 is applied.

Referring to FIG. 4, the technique for global classification comprises 5steps: 1) classify long tracks 40; 2) classify short tracks 44; 3)classify overlapping tracks 48; 4) fill in gaps 52; and 5) create afinal log 56. Two examples of how gaps 52 may be filled are usingnon-classified tracks to fill the gaps and using linear interpolation.Initially the long tracks are classified. A track is considered to be along track if the number of peaks in the track is greater than or equalto L. In one embodiment disclosed herein, the value of L corresponds to20 frames, which translate to 10 feet. This value corresponds to almost3 times the resolution of the array. However, it should be noted that Lcould be any number. It should also be noted that values of Lcorresponding to 20 frames were found to be sufficient to performstatistical analysis on the long track.

Then small tracks are classified. A track is considered to be a smalltrack if the number of peaks in the track is lower than L. Nextoverlapping tracks are classified. After the tracks are classified, gapsare filled in using either a small portion of the non-classified tracksor by interpolation in the case of small gaps. This enables theformation of a continuous log versus depth using all the informationavailable on the whole interval.

Models are used to classify the tracks. An embodiment uses models havinga 2D normal Gaussian distribution in the slowness and time domain. Afurther embodiment uses a 2D Kalman filter to determine the 2D Gaussianprobability distribution of slowness-time plane data. However, thechoice of the model is not meant to be restrictive and other models canbe used without deviating from the spirit and scope of the disclosedtechnique. The mean and the variance of these models for the time andthe slowness are determined based on the formation type and on theselected mode such as monopole or dipole mode. Typical formation typesare defined in PCT/IB00/00353 as fast, intermediate, slow, very slow andextremely slow. These formation types are illustrative and it will beappreciated that the present invention is not restricted to the use ofthese formation type descriptions. The typical mean and variance of thetime and slowness for formation type are determinable from other logdata obtained in a number of locations for the waveform arrival underconsideration and the selected transmitter mode such as monopole ordipole mode. It should be noted that the model data distribution will bethe same for all the different arrivals as the model is constructedusing peaks from all arrivals. Only the mean and the variance will varyaccording to the considered waveform arrival.

Long tracks are classified by evaluating how the distribution of thepeaks of the tracks matched with the model data. The first part of theprocessing involves fitting a Gaussian function on the data (histogramof the data). It should be noted that the actual data is assumed to besimilar distribution to the model data. FIG. 5 illustrates fitting aGaussian function model 60 on a histogram of data 64 corresponding to anactual track. The variance and the mean of the distribution of actualdata is then determined and compared to the mean and variance of themodel data. The distribution of the actual data is compared with themodel data distribution to define if the two models are consistent. Astatistical test may be used to evaluate the consistency and the resultof this test is the probability that the current track is consistentwith the considered model. FIGS. 6 a, 6 b and 6 c show examples of sucha comparison. If the actual track data 70 compares well with the modeldata 74 such as in FIG. 6 a, then the track is classified as the arrivaldefined by the model data. If the actual track data 70 does not comparewell with the model data 74 such as in FIG. 6 b, then the track cannotbe classified as the arrival defined by the model data; a differentwaveform model may be applied and compared. If as in FIG. 6 c, nowaveform model 74 fits the actual data 70, the arrival can be classifiedas a false alarm. The level of consistency between the data and themodel is an indicator of the level of confidence in the classificationof the track.

In case of small tracks, there are an insufficient number of peaks oneach track to evaluate the distribution of the peaks in a track as inthe case of long tracks. Therefore a different procedure is used. A 2-Dmedian of the track is computed. This point will be defined by aspecific coordinate in the slowness and time domain, defined asX_(m)(S), Y_(m)(t). This coordinate is used to represent the track inthe slowness and the time domain.

The slowness-time domain is then intersected with the model datadistribution. The model in the slowness time domain is defined as anellipse, or a circle if the variance of the slowness and the time arethe same. FIG. 7 shows such an intersection where compressional model 80is shown with shear model 82. The position of this peak, correspondingto the defined coordinate, relating to the model data determines how theconsidered track is classified. If the peak is inside the model, it willbe classified as the arrival related to the model. If the peak is notinside the model, it is not classified according to the model. Referringto FIG. 7, peak 84 is classified as a compressional peak, peak 86 isclassified as a shear peak and peak 88 is classified by computing itsrelative closeness to the center of the waveform model that contain it.

An issue in global classification concerns the overlap between twotracks. This case occurs when there are two tracks classified accordingto the same arrival, which are on the same depth interval. Threedifferent cases need to be considered depending on the relative positionof the different tracks (e.g. coextensive, overlapping, and separate).As it is already known the two tracks have the same arrival, the issuehere is not how to classify the tracks. Rather, the issue is selectingthe best part based on cohererence time and slowness information of theoverlapped tracks of each track to build the final log. The best part isselected by comparing the coherence values of the two tracks over all ofthe interval and selecting the track with the greatest degree ofcoherence.

After classifying all the long and small tracks, some tracks stillremain unused. These tracks were not used because they had smallprobability compared to others tracks or because they yielded a falsealarm; they are referred to as non-classified tracks. The classifiedtracks produce a skeleton of the final log. The skeleton and thenon-classified tracks are used to fill the existing gaps. The gaps arefilled based on the possible existing curves. If a monopole mode isconsidered, the gaps are filled for both compressional arrival and sheararrival. On the other hand, if Stoneley or dipole mode is used only onearrival needs to be checked.

A track is checked to determine if there is a gap at a certain depth. Ifa gap exists, then it is determined if whether an unused ornon-classified track may be used to fill the gap. Different tests areused to do this determination. Initially, it is determined if the trackin the slowness domain is between the upper part and the lower part ofthe skeleton. If the track is between the upper and lower part of theskeleton, then it is used to fill the gap. If the track is not betweenthe upper and the lower part of the gap in the log, a distance betweenthe track and the skeleton is measured to determine whether that segmentis compressional or shear to determine if it is appropriate to fill inthe track gaps. FIG. 8 shows an example of a non-classified track 92 maybe used to fill the gap in the classified compressional tracks 90 andclassified shear tracks 94. Depending on the mode considered, thedistance is deemed to be within a certain threshold in which case thetrack is classified. If the distance is beyond a certain threshold, thetrack is deleted.

In the example described herein, only one arrival is considered.However, it should be noted that depending on the mode used, allarrivals can be considered. It should be noted that only the slownesspart of the track is considered as an indicator for filling the gap. Atthis stage, time information is not used anymore; the slowness variableis used as a discriminating parameter related to the process.

If a gap in the track remains, interpolation may be used to fill thegap. Prior to this step, all the tracks built have been classified.Nevertheless, there might still be some gaps in the log due to theabsence of peaks. For example, at a given depth, no track may have beenbuilt or no track may have been classified. In one embodiment describedherein, a linear interpolation is made between tracks only if the gap isa small gap, that is, smaller or equal to 5 depth levels. In a furtherembodiment, the interpolation is linear. However, other interpolationscould be used without deviating from the spirit and scope of thedisclosed technique.

After all the tracks are classified and the gap filled, a final log ofslowness versus depth is generated which comprises the tracks outputfrom the global classification technique.

This way of classifying the tracks is different from prior art methodsof classifying, in that complete information on the whole interval isconsidered. By considering the information on the whole interval, jumpsand spikes on final logs, which may result from classification ofindividual peaks, are avoided. However, in the present method all thepeaks must be incorporated into raw tracks and raw slowness or timetracks generated for the entire depth before classification begins. Thisdata flow does not follow the data flow typical of sonic well loggingbut buffers with a certain number of levels and other softwaretechniques may be used for data storage and retrieval.

In the present global classification technique, the probability of atrack to be a compressional or a shear need not be evaluated using allthe points forming this track. That is, the probability of eachindividual peak need not be evaluated but rather the track is considereda single object comprising peaks. Also, the classification of one trackmay be independent from the others. Correlation between the differenttracks need not be considered. For example, in a monopole mode, a trackcould be classified as compressional arrival, shear arrival, or falsealarm. If the actual data could be fit to the waveform models such thatit could be either a compressional or shear arrival, it is considered asa false alarm. In a dipole mode, a track can be classified as sheararrival or Stoneley arrival or false alarm.

The global classification technique may be implemented in a computersystem. Preferably, the invention is implemented in computer programsexecuting on programmable computers each comprising a processor, a datastorage system (including memory and storage elements), at least oneinput device, and at least one output device. Program code is applied toinput data to perform the functions described above and generate outputinformation. Program code may be implemented in a computer programwritten in a programming language to communicate with a computer system.

Each such computer program is may be stored on a storage media or device(e.g., ROM or magnetic/optical disk or diskette) readable by a generalor special purpose programmable computer, for configuring and operatingthe computer when the storage media or device is read by the computer toperform the procedures described herein. The technique may also beconsidered to be implemented as a computer-readable storage mediumconfigured with a computer program, where the storage medium soconfigured causes a computer to operate in a specific and predefinedmanner to perform the functions described herein.

Other modifications and variations to the invention will be apparent tothose skilled in the art from the foregoing disclosure and teachings.Likewise while a particular apparatus has been described, it will beappreciated that other types and different numbers of sources andreceivers could be utilized. Similarly, it will be appreciated that theprocessing means for processing the obtained wave signals can take anyof numerous forms such as a computer, dedicated circuitry, etc.Therefore while only certain embodiments of the invention have beenspecifically described herein, it will be apparent that numerousmodifications may be made thereto without departing from the spirit andscope of the invention.

1. A method of determining the sonic slowness of formation traversed bya borehole comprising: generating tracks from sonic waveform peaksreceived at more than two depths; and classifying the generated tracks,wherein the step of classifying is not performed prior to the step ofgenerating tracks, wherein said step of classifying tracks comprisesclassifying long tracks, classifying small tracks, classifying tracksthat overlap, filling in gaps and creating a final log.
 2. The method ofclaim 1, wherein said filling in gaps further comprises usingnon-classified tracks to fill gaps.
 3. The method of claim 1, whereinsaid filling in gaps further comprises performing interpolation.
 4. Themethod of claim 2, wherein said interpolation is linear.
 5. The methodof claim 3 wherein linear interpolation is done if the gaps are lessthan 6 frames.
 6. The method of claim 2 wherein filling in gaps furthercomprising performing interpolation.
 7. The method of claim 1, whereintracks are considered as individual objects comprising peaks.
 8. Themethod of claim 6 wherein said peaks are defined using semblance, timeand slowness.
 9. The method of claim 7 wherein only time and slownessare used for classification.
 10. The method of claim 8, wherein aprobability of a track being one of a compressional and shear isdetermined using all points forming the track.
 11. The method of claim9, wherein classification of one track is independent of classificationof a track different from said one track.
 12. The method of claim 1,wherein step of classifying the long tracks further comprises: fitting adistribution function on peaks of the track; calculating a mean andvariance of the distribution; comparing distribution of the data with adistribution of a model data; and classifying the long track accordingto the model data if said comparison determines that the track data andmodel data are consistent.
 13. The method of claim 1 wherein step ofclassifying the short tracks further comprises: computing a 2-D medianof the track, said median being a point defined by correspondingcoordinates in a slowness and time domain; determining an intersectionof the slowness and time domain with a model data distribution; definingthe model in the slowness and time domain as an ellipse; and classifyingthe small track based on a position of the peak in relation to the modeldata.
 14. The method of claim 1, wherein step of filling in the gapsfurther comprises: determining if there is a gap in a selected track ata depth range covered by the selected non-classified track; deleting thetrack if no gap is found; and filling the gap in the selected trackafter determining that the selected non-classified track can be used tofill the gap.
 15. The method of claim 13, wherein said determining ifthe selected track can be used to fill the gap is done by evaluating ifthe selected track is between upper part and lower part of a skeleton,wherein said skeleton comprises tracks that have been classified so far.16. The method of claim 1, wherein said long track comprises more than20 frames.
 17. The method of claim 1, wherein said small track comprisesless than or equal to 20 frames.
 18. The method of claim 12 wherein saidmodel is one of a compressional model and shear model.
 19. The method ofclaim 11 wherein slowness arid time are treated having Gaussianprobability distribution.
 20. The method of claim 18 wherein 2D Gaussianprobability distribution of slowness and time is measured at one depthbased on measurements at a previous depth.
 21. The method of claim 18wherein said measurement is don by a 2D Kaman filter process.
 22. Acomputer system for performing a method of determining the sonicslowness of a formation traversed by a borehole comprising: generatingtracks from sonic waveform peaks received at two or more depths; andclassifying the generated tracks wherein the step of classifying is notperformed prior to the step of generating track, wherein said step ofclassifying tracks comprises classifying long tracks, classifying smalltracks, classifying tracks that overlap, filling in gaps and creating afinal log, wherein the method is implemented in a program stored on astorage media and the output is applied to at least one output device.23. A method of determining the sonic slowness of a formation traversedby a borehole comprising generating tracks from sonic waveform peaksreceived at a plurality of depths, comprising a) classifying long tracksof greater than 20 frames, further comprising fitting a distributionfunction on peaks of the track; calculating a mean and variance of thedistribution; comparing distribution of the data with a distribution ofa model data; and classifying the long track according to the model dataif said comparison determines that the track data and model data areconsistent; b) classifying small tracks of less than or equal to 20frames, further comprising computing a 2-D median of the track, saidmedian being a point defined by corresponding coordinates in a slownessand time domain; determining an intersection of slowness and time domainwith a model data distribution; defining the model in the slowness andtime domain as an ellipse; and classifying the small track based on aposition of the peak in relation to the model data; c) classifyingtracks that overlap, wherein said steps of classifying long tracks,small tracks and tracks that overlap are not performed prior to trackingof sonic waveform peaks received at more than two depths; d) filling inthe gaps, further comprising determining if there is a gap in a selectedtrack at a depth range covered by a selected non-classified track;deleting the track if no gap is found; and filling the gap in theselected track after determining that the selected non-classified trackcan be used to fill the gap; and e) creating a final log.